1. Field of the Invention
This invention relates to the recognition of images and is concerned with the recognition of both natural and synthetic images.
By “natural image” is meant an image of an object that occurs naturally—for example, an optical image such as a photograph, as well as images of other wavelengths—such as x-ray and infra-red, by way of example. The natural image may be recorded and/or subsequently processed by digital means, but is in contrast to an image—or image data—that is generated or synthesised by computer or other artificial means.
2. Description of Related Art
The recognition of natural images can be desirable for many reasons. For example, distinctive landscapes and buildings can be recognised, to assist in the identification of geographical locations. The recognition of human faces can be useful for identification and security purposes. The recognition of valuable animals such as racehorses may be very useful for identification purposes.
In this specification, we present in preferred embodiments of the invention a new approach to face recognition using a variety of three-dimensional facial surface representations generated from a University of York (UofY)/Cybula 3D Face Database.
Despite significant advances in face recognition technology, it has yet to achieve the levels of accuracy required for many commercial and industrial applications. Although some face recognition systems proclaim extremely low error rates in the test environment, these figures often increase when exposed to a real world scenario. The reasons for these high error rates stem from a number of well-known sub-problems that have never been fully solved. Face recognition systems are highly sensitive to the environmental circumstances under which images are captured. Variation in lighting conditions, facial expression and orientation can all significantly increase error rates, making it necessary to maintain consistent image capture conditions between query and gallery images for the system to function adequately. However, this approach eliminates some of the key advantages offered by face recognition: a passive biometric in the sense that it does not require subject co-operation.